Espinosa-Anke, Luis  ORCID: https://orcid.org/0000-0001-6830-9176 and Schockaert, Steven ORCID: https://orcid.org/0000-0002-9256-2881
2018.
Syntactically aware neural architectures for definition extraction.
Presented at: 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,
New Orleans, LA, US,
1-6 June 2018.
Published in: Walker, Marilyn, Ji, Heng and Stent, Amanda eds.
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics.
, vol.2
pp. 378-385.
10.18653/v1/N18-2061
|
Preview |
PDF
- Accepted Post-Print Version
Download (240kB) | Preview |
Abstract
Automatically identifying definitional knowledge in text corpora (Definition Extraction or DE) is an important task with direct applications in, among others, Automatic Glossary Generation, Taxonomy Learning, Question Answering and Semantic Search. It is generally cast as a binary classification problem between definitional and non-definitional sentences. In this paper we present a set of neural architectures combining Convolutional and Recurrent Neural Networks, which are further enriched by incorporating linguistic information via syntactic dependencies. Our experimental results in the task of sentence classification, on two benchmarking DE datasets (one generic, one domain-specific), show that these models obtain consistent state of the art results. Furthermore, we demonstrate that models trained on clean Wikipedia-like definitions can successfully be applied to more noisy domain-specific corpora.
| Item Type: | Conference or Workshop Item - published (Paper) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| ISBN: | 9781948087292 |
| Related URLs: | |
| Date of First Compliant Deposit: | 27 June 2018 |
| Last Modified: | 03 Aug 2026 05:45 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/111116 |
Citation Data
Cited 28 times in Scopus. View in Scopus. Powered By Scopus® Data
Actions (repository staff only)
 |
Edit Item |
Altmetric
Altmetric